
"""simple docstring"""


import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Dict				=      OpenAIGPTTokenizer
       UpperCamelCase_		:       Union[str, Any]				=      OpenAIGPTTokenizerFast
       UpperCamelCase_		:       int				=      True
       UpperCamelCase_		:       Any				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Union[str, Any]:







              """simple docstring"""
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase       			=		[
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  "<unk>",
              ]
              UpperCamelCase       			=		dict(zip(UpperCAmelCase_							,   range(len(UpperCAmelCase_      )      )      )      )
              UpperCamelCase       			=		["#version: 0.2", "l o", "lo w", "e r</w>", ""]

              UpperCamelCase       			=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["vocab_file"]      )
              UpperCamelCase       			=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["merges_file"]      )
              with open(self.vocab_file							,   "w"      ) as fp:
                     fp.write(json.dumps(UpperCAmelCase_      )      )
              with open(self.merges_file							,   "w"      ) as fp:
                     fp.write("\n".join(UpperCAmelCase_      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				str      )->	Any:







              """simple docstring"""
              return "lower newer", "lower newer"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		OpenAIGPTTokenizer(self.vocab_file							,   self.merges_file      )

              UpperCamelCase       			=		"lower"
              UpperCamelCase       			=		["low", "er</w>"]
              UpperCamelCase       			=		tokenizer.tokenize(UpperCAmelCase_      )
              self.assertListEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokens + ["<unk>"]
              UpperCamelCase       			=		[14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]=15      )->	Dict:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )

                            # Simple input
                            UpperCamelCase       			=		"This is a simple input"
                            UpperCamelCase       			=		["This is a simple input 1", "This is a simple input 2"]
                            UpperCamelCase       			=		("This is a simple input", "This is a pair")
                            UpperCamelCase       			=		[
                                ("This is a simple input 1", "This is a simple input 2"),
                                ("This is a simple pair 1", "This is a simple pair 2"),
                            ]

                            # Simple input tests
                            self.assertRaises(UpperCAmelCase_							,   tokenizer_r.encode							,   UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   padding="max_length"      )

                            # Simple input
                            self.assertRaises(UpperCAmelCase_							,   tokenizer_r.encode_plus							,   UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   padding="max_length"      )

                            # Simple input
                            self.assertRaises(
                                UpperCAmelCase_							,   tokenizer_r.batch_encode_plus							,   UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   padding="max_length"							,   )

                            # Pair input
                            self.assertRaises(UpperCAmelCase_							,   tokenizer_r.encode							,   UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   padding="max_length"      )

                            # Pair input
                            self.assertRaises(UpperCAmelCase_							,   tokenizer_r.encode_plus							,   UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   padding="max_length"      )

                            # Pair input
                            self.assertRaises(
                                UpperCAmelCase_							,   tokenizer_r.batch_encode_plus							,   UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   padding="max_length"							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Union[str, Any]:







              """simple docstring"""
              pass





@require_ftfy
@require_spacy
@require_tokenizers
class 	__a	(   _lowerCAmelCase						):
       pass







"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      ['''image_processor''', '''tokenizer''']
       UpperCamelCase_		:       Dict				=      '''BlipImageProcessor'''
       UpperCamelCase_		:       Optional[int]				=      '''AutoTokenizer'''






       def __init__(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_							,   UpperCAmelCase_      )

              # add QFormer tokenizer
              UpperCamelCase       			=		qformer_tokenizer






       def __call__(	self			:				Tuple							,   UpperCAmelCase_			:				ImageInput = None							,   UpperCAmelCase_			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Union[bool, str, PaddingStrategy] = False							,   UpperCAmelCase_			:				Union[bool, str, TruncationStrategy] = None							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Optional[Union[str, TensorType]] = None							,   **UpperCAmelCase_			:				str							,   )->	BatchFeature:







              """simple docstring"""
              if images is None and text is None:
                     raise ValueError("You have to specify at least images or text."      )

              UpperCamelCase       			=		BatchFeature()

              if text is not None:
                     UpperCamelCase       			=		self.tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     encoding.update(UpperCAmelCase_      )
                     UpperCamelCase       			=		self.qformer_tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     UpperCamelCase       			=		qformer_text_encoding.pop("input_ids"      )
                     UpperCamelCase       			=		qformer_text_encoding.pop("attention_mask"      )

              if images is not None:
                     UpperCamelCase       			=		self.image_processor(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_      )
                     encoding.update(UpperCAmelCase_      )

              return encoding






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				List[str]      )->	Dict:







              """simple docstring"""
              return self.tokenizer.batch_decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   *UpperCAmelCase_			:				Dict							,   **UpperCAmelCase_			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              return self.tokenizer.decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer.model_input_names
              UpperCamelCase       			=		self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Tuple							,   **UpperCAmelCase_			:				Tuple      )->	Union[str, Any]:







              """simple docstring"""
              if os.path.isfile(UpperCAmelCase_      ):
                     raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"      )
              os.makedirs(UpperCAmelCase_							,   exist_ok=UpperCAmelCase_      )
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_							,   "qformer_tokenizer"      )
              self.qformer_tokenizer.save_pretrained(UpperCAmelCase_      )
              return super().save_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Dict							,   UpperCAmelCase_			:				int							,   **UpperCAmelCase_			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		AutoTokenizer.from_pretrained(UpperCAmelCase_							,   subfolder="qformer_tokenizer"      )
              UpperCamelCase       			=		cls._get_arguments_from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
              args.append(UpperCAmelCase_      )
              return cls(*UpperCAmelCase_      )





